Harnessing AI for decarbonization of office buildings

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Commercial real estate (CRE) finds itself in the middle of a climate crisis fueled by the massive carbon footprint of buildings. Within the United States alone, commercial buildings are responsible for a staggering 16% of all CO2 emissions and 35% of electricity consumption. Even more alarmingly, these structures waste an average of 30% of their energy output.

The urgency to decarbonize the built environment is crystal clear. And while it’s tempting to focus on innovating in construction, improving the energy efficiency of existing buildings is key. Given the typical 30- to 130-years lifetime of a building, 80% of predicted building stock for 2050 has already been built.

Climate technology is emerging as a guiding force within the CRE industry to improve the energy efficiency of these assets. The spark for this transformative shift is artificial intelligence (AI), a versatile tool that has already demonstrated its abilities across various sectors. AI is now the biggest spend for nearly 50% of top tech execs across the economy. Employees are bullish about AI too.

31% of employees in a recent PwC survey felt ‘AI will help me increase my productivity/efficiency at work’ and 27% felt it ‘AI will create opportunities for me to learn new skills’.

PwC’s Global Workforce Hopes and Fears Survey 2023

But not all AI is equal. The ‘right data’ connected to the right systems is important for AI success and shouldn’t be taken for granted. As is building on the systems and processes you already have in place. To get the best performance out of your building – read the highest energy and carbon emissions savings while meeting regulatory targets – you want tools geared for office portfolios and the way your teams operate.

In this article, we’ll dive into the potential of AI for decarbonization of office portfolios and what to consider as you assess AI solutions.

The difference between predictive AI and generative AI

Predictive AI focuses on performing a specific task intelligently. It refers to systems designed to respond to inputs. Predictive AI continuously learns from data to discover insights, identify patterns, and forecast outcomes

Generative AI, on the other hand, can create something new from a piece of information. Think ChatGPT – the generative AI – and, conversational topic of 2023.

When we talk about AI in this article, we’re referring to predictive AI.

Commercial real estate and decarbonization technology

Historically, the commercial real estate sector has been slow to adopt new technologies compared to other industries. People-centered – there have been fears that technology will take away from the expertise of staff at the heart of buildings. Married with an overwhelming selection of technology options, it can seem impossible to make the jump to the right solution.

But to stay relevant in a rapidly changing and challenging market, commercial real estate must embrace technology. Especially when faced with the transformative power of artificial intelligence. AI will change how commercial real estate stakeholders operate, bringing efficiency and innovation.

This is particularly important, when we consider that more than half of people working in facilities management today are expected to retire in the next five to 15 years. When they go, their institutional knowledge will leave with them unless you’ve taken steps to capture it.

“The workforce is important. The average age of our building operators is 55 years old, so they know these buildings like the back of their hands. I need to figure out how to get the back of their hands into the cloud, which is part of what this entire (smart buildings) program is.”

Thano Lambrinos, Senior Vice President – Digital Buildings, Experiences & Innovation at QuadReal Property Group

A tangible example of AI’s impact on CRE can be seen in energy management systems. Adding a layer of intelligence to the Building Management System (BMS), AI-driven platforms enable building managers to closely track energy consumption in their properties. Armed with real-time insights, they can fine-tune energy usage, reduce energy loss, and enhance overall efficiency.

How to evaluate an AI-driven decarbonization platform

Choosing the right AI solutions for your portfolio can seem daunting, even if you’re confident that it’ll save money, carbon emissions, and energy. As you research your options to help you on the path to decarbonization, consider the following:

Where’s the data coming from?

Data plays a significant role in the AI revolution. After all, the data used to train AI is all the system knows about the world and what it learns from to perform its tasks. Furthermore, we shouldn’t take the availability of this data for granted. Real-world operational data for office buildings isn’t easily accessible at scale. For various reasons, this data is historically not saved or, often, not captured at all.

Think of AI models like cars. It’s all about what’s under the hood – the engine. Many vehicles come with a stock engine and perform adequately. This is analogous to generic AI models built on static, industry data. But the same way a car mechanic can tune an engine for optimal performance, we can fine-tune AI models for performance. With large, tailored datasets and first-party data from your BMS and utilities, AI models can provide more accurate, more granular, and more actionable insights. Ultimately, this opens up possibilities for better decision-making and more streamlined processes.

For instance, Cortex’s Base Building Intelligence uses AI to optimize energy management based on years (and years, and years) of collecting data from Class A & B office buildings. Our exclusive reservoir of trillions of data points, gives organizations a distinct advantage over energy management tools that are just getting started or are pulling from static industry datasets.

How helpful is the data?

Dig into whether the platform produces impactful, actionable insights. It’s all good and well for AI-driven platforms to surface data. It’s a whole other thing, if they leave your team asking – what to do next? Faced with the daily juggle and the increasing complexity of buildings, it’s a big ask for building teams to also translate energy data into specific next steps.

“If you’re managing a building, knowing exactly what time to start coasting chillers or to start your turndown sequence is meaningful. Rather than shooting from the hip, you’re making daily, data driven decisions. These decisions translate into huge energy savings over the course of a year.”

Zack Nelson, Senior Vice President of Product Development, Cortex

The goal here is to reduce your team’s work, not make more of it. Be sure that you’re getting a granular list of what to do – across the HVAC horizon – that reflects current conditions in your building.

Will it replace your team? (Should it?)

Lots of the hype around AI is the idea that it might replace people across entire occupations. And while AI is developing at speed, it’s important to maintain a perspective on what it can currently do. While AI is adept at recognizing patterns, assimilating data, and making predictions, it can’t replace human judgment and decision making.

AI is a supporting tool to the work your building team is already doing. It can start up your building; it can identify equipment that ran overnight; it can make operational recommendations. But without the human assessment and context of those outputs – it’s just not as valuable.

Zack Nelson, Senior Vice President of Product Development, Cortex

Be wary of companies who promise to single-handedly take over managing your HVAC systems and put your building engineering team out of work. It’s crucial to work with tools that acknowledge the balance between boots-on-the-ground expertise and AI-driven optimization.

Companies tackling carbon emissions with AI

Savanna, a leading vertically-integrated real estate investment firm in New York, saw significant improvements in HVAC operations and energy efficiency across their portfolio by implementing Cortex Base Building Intelligence.

They’re not alone in their success. Our other clients, such as Empire State Realty Trust, Silverstein Properties, and CBRE, have also experienced similar results by implementing Cortex Base Building Intelligence.

Amplify energy and carbon emissions savings with AI

AI has made rapid advances in the past few years. This has an undeniable impact on the way we approach decarbonization in commercial real estate. Especially when we consider improving the energy efficiency of existing building stock. But not all AI tools are the same. The key to making the most out of this transformative technology is to find the right solution for your portfolio. By asking the right questions — like where the data comes from, the insights it produces, and how it works with human judgment — you can confidently integrate AI into your buildings.

Working hand-in-algorithms with AI-driven solutions like Cortex, you can reduce your environmental impact and carbon footprint, show meaningful ROI, and demonstrate action to those who matter.

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